ZuSocial @Istanbul - Nov 15th Daily Roundup

1/ Data science & Machine learning 101

ZuSocial Hacker: Akira @akirawuc

What is data and machine learning?

Data is actually relative to different people. The data you choose to represent / simplify the world / task, directly reflect what you are seeking for. (Personally, data is piece of information with structured form.)

Machine learning involves the process of extracting/ simplifying the world into data, finding patterns through the data, and fitting it to the real world.

The DL/ ML/ AI universe and difference between rule-based system, classic ML, and representation learning

Deep learning, Machine learning, AI, etc. there are too many terms and below graph shows a clear relation between different concepts.

Compared to rule-based system, Classic ML mainly includes mapping from features before the output. While Representation learning system extracts features from raw data or use multiple layers of extracted features to replace hand-designed features.

What is a learning algorithm?

It is defined as a computer program that improves its performance at tasks in T, as measured by P, improves with experience E.

2/ XMTP workshop - The secure messaging network for Web3

Guest: Fabri Guespe @fabriguespe from XMTP @xmtp_

The problem XMTP tries to tackle

• Developers can't reach users

• Creators can't reach their audiences

• Users can't reach each other

XMTP's solution and use cases

XMTP (Extensible Message Transport Protocol) is an open protocol, network, and standard for secure, private web3 messaging. It serves as a base protocol. Developers can build with XMTP SDKs to provide messaging between blockchain accounts in their apps.

XMTP's use cases include:

• Interoperable inboxes

  • Coinbase wallet, Converse, Lenster, all use XMTP in the backend

• Other use cases

  • CRM tools for creators to reach NFT holders

  • Community-driven social media super app, e.g. Orb

  • Bots that can handle complex interactions like smart contract

  • Global payments over messaging, e.g. Coinbase

  • Timely alerts that drive action, e.g. Snapshot

  • Matchmaking based on on-chain interactions, e.g. Converse

Working on protocol v3

Some features under development include:

• Double-ratchet messaging for foward secrecy & post-compromise security

• libxmtp for transparent protocol development

• Group chats

• Consent features

• Decentralization

Relevant link

• Doc: https://xmtp.org/docs/introduction

• Official site: https://xmtp.org/

3/ Lens workshop - Building full stack DeSo apps

Guest: Nader Dabit @dabit3 from @LensProtocol

What is Lens?

Lens offers a protocol and a suite of tools and APIs for building social apps or integrating social features into existing apps. Social app involves creating a profile, following, publishing, viewing feed of other comments, and recommendation algo

Lens V2

• Lens launched V2 on Nov 13th and V2 will include several new features:

  • Publication metadata: V2 introduces a native way to enable Quoted Publications

  • Separate profiles and handles: ERC 6551 powers profile as a wallet and profile becomes the core identity for all actions

  • Smart posts (previously called Open actions): V2 facilitates smart contract interaction on any protocol, smart contract, or network with an existing publication, incl. buying nft, staking, voting etc.

  • New referral system: V2 allows rewarding those that helped to discover a publication, reward original posters for any activity that happens after, and reward apps and UIs used to interact with Lens, and help discovery

  • Profile manager: V2 allows delegation of social actions to a different wallet

How does Lens work

• Set of smart contracts

• Deployed on polygon

• GraphQL API for quering capabilities

• Gasless transactions via relayer for whitelisted front ends

• Dispatcher for seamless interaction

• High quality dev experience

Relevant link

• Launch of Lens V2: https://mirror.xyz/lensprotocol.eth/Kc1AkZYWX1kR6XCsOf0bpU3pbXi0Juayt1tPyPicv4c

• Official site: https://www.lens.xyz/

4/ Recommendation system workshop

ZuSocial Hacker: Yassime @YassineLanda

Recommendation system in current social media

• Benefit of the recommendation system: It helps grow the network more dense in a faster way

• Disadvantage: It can be very addictive for users.

How does feed ranking actually work?

2010 formula:

• Priority (user, item) = affinity (user, poster) * weight [item, type] / item.age

2017 formula:

• MSI (user, item) = affinity (user, poster) ∑ P (user, item, int-type) weight [int - type]

• MSI: meaningful social interaction, which prioritizes interactions, such as comments and likes, between friends and family. The idea was to give more weight to the posts and engagements of people that social media platform thought are closest to users.

Personalization best practice

It's all about interactions! Best practice includes:

• Quantify interactions

• Predicting interest

• Personalization

Limitation of data-based algorithm

There are certain aspects of the impact/ value, especially long-term ones, difficult to be represented by data currently, thus hard to measure.